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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code>.Birch</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-cluster-birch">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cluster.Birch</span></code></a></li>
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  <div class="section" id="sklearn-cluster-birch">
<h1><a class="reference internal" href="../classes.html#module-sklearn.cluster" title="sklearn.cluster"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code></a>.Birch<a class="headerlink" href="#sklearn-cluster-birch" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.cluster.Birch">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.cluster.</code><code class="sig-name descname">Birch</code><span class="sig-paren">(</span><em class="sig-param">threshold=0.5</em>, <em class="sig-param">branching_factor=50</em>, <em class="sig-param">n_clusters=3</em>, <em class="sig-param">compute_labels=True</em>, <em class="sig-param">copy=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/cluster/_birch.py#L322"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.Birch" title="Permalink to this definition">¶</a></dt>
<dd><p>Implements the Birch clustering algorithm.</p>
<p>It is a memory-efficient, online-learning algorithm provided as an
alternative to <a class="reference internal" href="sklearn.cluster.MiniBatchKMeans.html#sklearn.cluster.MiniBatchKMeans" title="sklearn.cluster.MiniBatchKMeans"><code class="xref py py-class docutils literal notranslate"><span class="pre">MiniBatchKMeans</span></code></a>. It constructs a tree
data structure with the cluster centroids being read off the leaf.
These can be either the final cluster centroids or can be provided as input
to another clustering algorithm such as <a class="reference internal" href="sklearn.cluster.AgglomerativeClustering.html#sklearn.cluster.AgglomerativeClustering" title="sklearn.cluster.AgglomerativeClustering"><code class="xref py py-class docutils literal notranslate"><span class="pre">AgglomerativeClustering</span></code></a>.</p>
<p>Read more in the <a class="reference internal" href="../clustering.html#birch"><span class="std std-ref">User Guide</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.16.</span></p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>threshold</strong><span class="classifier">float, default 0.5</span></dt><dd><p>The radius of the subcluster obtained by merging a new sample and the
closest subcluster should be lesser than the threshold. Otherwise a new
subcluster is started. Setting this value to be very low promotes
splitting and vice-versa.</p>
</dd>
<dt><strong>branching_factor</strong><span class="classifier">int, default 50</span></dt><dd><p>Maximum number of CF subclusters in each node. If a new samples enters
such that the number of subclusters exceed the branching_factor then
that node is split into two nodes with the subclusters redistributed
in each. The parent subcluster of that node is removed and two new
subclusters are added as parents of the 2 split nodes.</p>
</dd>
<dt><strong>n_clusters</strong><span class="classifier">int, instance of sklearn.cluster model, default 3</span></dt><dd><p>Number of clusters after the final clustering step, which treats the
subclusters from the leaves as new samples.</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code> : the final clustering step is not performed and the
subclusters are returned as they are.</p></li>
<li><p><a class="reference internal" href="../classes.html#module-sklearn.cluster" title="sklearn.cluster"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code></a> Estimator : If a model is provided, the model
is fit treating the subclusters as new samples and the initial data
is mapped to the label of the closest subcluster.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">int</span></code> : the model fit is <a class="reference internal" href="sklearn.cluster.AgglomerativeClustering.html#sklearn.cluster.AgglomerativeClustering" title="sklearn.cluster.AgglomerativeClustering"><code class="xref py py-class docutils literal notranslate"><span class="pre">AgglomerativeClustering</span></code></a> with
<code class="docutils literal notranslate"><span class="pre">n_clusters</span></code> set to be equal to the int.</p></li>
</ul>
</dd>
<dt><strong>compute_labels</strong><span class="classifier">bool, default True</span></dt><dd><p>Whether or not to compute labels for each fit.</p>
</dd>
<dt><strong>copy</strong><span class="classifier">bool, default True</span></dt><dd><p>Whether or not to make a copy of the given data. If set to False,
the initial data will be overwritten.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>root_</strong><span class="classifier">_CFNode</span></dt><dd><p>Root of the CFTree.</p>
</dd>
<dt><strong>dummy_leaf_</strong><span class="classifier">_CFNode</span></dt><dd><p>Start pointer to all the leaves.</p>
</dd>
<dt><strong>subcluster_centers_</strong><span class="classifier">ndarray,</span></dt><dd><p>Centroids of all subclusters read directly from the leaves.</p>
</dd>
<dt><strong>subcluster_labels_</strong><span class="classifier">ndarray,</span></dt><dd><p>Labels assigned to the centroids of the subclusters after
they are clustered globally.</p>
</dd>
<dt><strong>labels_</strong><span class="classifier">ndarray, shape (n_samples,)</span></dt><dd><p>Array of labels assigned to the input data.
if partial_fit is used instead of fit, they are assigned to the
last batch of data.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.cluster.MiniBatchKMeans.html#sklearn.cluster.MiniBatchKMeans" title="sklearn.cluster.MiniBatchKMeans"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MiniBatchKMeans</span></code></a></dt><dd><p>Alternative  implementation that does incremental updates of the centers’ positions using mini-batches.</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>The tree data structure consists of nodes with each node consisting of
a number of subclusters. The maximum number of subclusters in a node
is determined by the branching factor. Each subcluster maintains a
linear sum, squared sum and the number of samples in that subcluster.
In addition, each subcluster can also have a node as its child, if the
subcluster is not a member of a leaf node.</p>
<p>For a new point entering the root, it is merged with the subcluster closest
to it and the linear sum, squared sum and the number of samples of that
subcluster are updated. This is done recursively till the properties of
the leaf node are updated.</p>
<p class="rubric">References</p>
<ul class="simple">
<li><p>Tian Zhang, Raghu Ramakrishnan, Maron Livny
BIRCH: An efficient data clustering method for large databases.
<a class="reference external" href="https://www.cs.sfu.ca/CourseCentral/459/han/papers/zhang96.pdf">https://www.cs.sfu.ca/CourseCentral/459/han/papers/zhang96.pdf</a></p></li>
<li><p>Roberto Perdisci
JBirch - Java implementation of BIRCH clustering algorithm
<a class="reference external" href="https://code.google.com/archive/p/jbirch">https://code.google.com/archive/p/jbirch</a></p></li>
</ul>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">Birch</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mf">0.3</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.3</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mf">0.3</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">brc</span> <span class="o">=</span> <span class="n">Birch</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">brc</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="go">Birch(n_clusters=None)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">brc</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="go">array([0, 0, 0, 1, 1, 1])</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.Birch.fit" title="sklearn.cluster.Birch.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(self, X[, y])</p></td>
<td><p>Build a CF Tree for the input data.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.Birch.fit_predict" title="sklearn.cluster.Birch.fit_predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_predict</span></code></a>(self, X[, y])</p></td>
<td><p>Perform clustering on X and returns cluster labels.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.Birch.fit_transform" title="sklearn.cluster.Birch.fit_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_transform</span></code></a>(self, X[, y])</p></td>
<td><p>Fit to data, then transform it.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.Birch.get_params" title="sklearn.cluster.Birch.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>(self[, deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.Birch.partial_fit" title="sklearn.cluster.Birch.partial_fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">partial_fit</span></code></a>(self[, X, y])</p></td>
<td><p>Online learning.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.Birch.predict" title="sklearn.cluster.Birch.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(self, X)</p></td>
<td><p>Predict data using the <code class="docutils literal notranslate"><span class="pre">centroids_</span></code> of subclusters.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.Birch.set_params" title="sklearn.cluster.Birch.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(self, \*\*params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.Birch.transform" title="sklearn.cluster.Birch.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(self, X)</p></td>
<td><p>Transform X into subcluster centroids dimension.</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.cluster.Birch.__init__">
<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">threshold=0.5</em>, <em class="sig-param">branching_factor=50</em>, <em class="sig-param">n_clusters=3</em>, <em class="sig-param">compute_labels=True</em>, <em class="sig-param">copy=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/cluster/_birch.py#L433"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.Birch.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.cluster.Birch.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/cluster/_birch.py#L441"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.Birch.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Build a CF Tree for the input data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix}, shape (n_samples, n_features)</span></dt><dd><p>Input data.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present here for API consistency by convention.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>self</dt><dd><p>Fitted estimator.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.cluster.Birch.fit_predict">
<code class="sig-name descname">fit_predict</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L443"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.Birch.fit_predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Perform clustering on X and returns cluster labels.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">ndarray, shape (n_samples, n_features)</span></dt><dd><p>Input data.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present for API consistency by convention.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>labels</strong><span class="classifier">ndarray, shape (n_samples,)</span></dt><dd><p>Cluster labels.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.cluster.Birch.fit_transform">
<code class="sig-name descname">fit_transform</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em>, <em class="sig-param">**fit_params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L544"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.Birch.fit_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit to data, then transform it.</p>
<p>Fits transformer to X and y with optional parameters fit_params
and returns a transformed version of X.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">numpy array of shape [n_samples, n_features]</span></dt><dd><p>Training set.</p>
</dd>
<dt><strong>y</strong><span class="classifier">numpy array of shape [n_samples]</span></dt><dd><p>Target values.</p>
</dd>
<dt><strong>**fit_params</strong><span class="classifier">dict</span></dt><dd><p>Additional fit parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_new</strong><span class="classifier">numpy array of shape [n_samples, n_features_new]</span></dt><dd><p>Transformed array.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.cluster.Birch.get_params">
<code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L173"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.Birch.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">mapping of string to any</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.cluster.Birch.partial_fit">
<code class="sig-name descname">partial_fit</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X=None</em>, <em class="sig-param">y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/cluster/_birch.py#L528"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.Birch.partial_fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Online learning. Prevents rebuilding of CFTree from scratch.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix}, shape (n_samples, n_features), None</span></dt><dd><p>Input data. If X is not provided, only the global clustering
step is done.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present here for API consistency by convention.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>self</dt><dd><p>Fitted estimator.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.cluster.Birch.predict">
<code class="sig-name descname">predict</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/cluster/_birch.py#L564"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.Birch.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict data using the <code class="docutils literal notranslate"><span class="pre">centroids_</span></code> of subclusters.</p>
<p>Avoid computation of the row norms of X.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix}, shape (n_samples, n_features)</span></dt><dd><p>Input data.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>labels</strong><span class="classifier">ndarray, shape(n_samples)</span></dt><dd><p>Labelled data.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.cluster.Birch.set_params">
<code class="sig-name descname">set_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L205"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.Birch.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s possible to update each
component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.cluster.Birch.transform">
<code class="sig-name descname">transform</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/cluster/_birch.py#L587"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.Birch.transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Transform X into subcluster centroids dimension.</p>
<p>Each dimension represents the distance from the sample point to each
cluster centroid.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix}, shape (n_samples, n_features)</span></dt><dd><p>Input data.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_trans</strong><span class="classifier">{array-like, sparse matrix}, shape (n_samples, n_clusters)</span></dt><dd><p>Transformed data.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

</dd></dl>

<div class="section" id="examples-using-sklearn-cluster-birch">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cluster.Birch</span></code><a class="headerlink" href="#examples-using-sklearn-cluster-birch" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="This example compares the timing of Birch (with and without the global clustering step) and Min..."><div class="figure align-default" id="id1">
<img alt="../../_images/sphx_glr_plot_birch_vs_minibatchkmeans_thumb.png" src="../../_images/sphx_glr_plot_birch_vs_minibatchkmeans_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_birch_vs_minibatchkmeans.html#sphx-glr-auto-examples-cluster-plot-birch-vs-minibatchkmeans-py"><span class="std std-ref">Compare BIRCH and MiniBatchKMeans</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows characteristics of different clustering algorithms on datasets that are &quot;int..."><div class="figure align-default" id="id2">
<img alt="../../_images/sphx_glr_plot_cluster_comparison_thumb.png" src="../../_images/sphx_glr_plot_cluster_comparison_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_cluster_comparison.html#sphx-glr-auto-examples-cluster-plot-cluster-comparison-py"><span class="std std-ref">Comparing different clustering algorithms on toy datasets</span></a></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</div>
</div><div class="clearer"></div></div>
</div>


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